Building Genetic Scores to Predict Risk of Complex Diseases in Humans: Is It Possible?

  1. Yiqing Song5
  1. 1Program on Genomics and Nutrition, Department of Epidemiology, University of California, Los Angeles, Los Angeles, California;
  2. 2Center for Metabolic Disease Prevention, University of California, Los Angeles, Los Angeles, California;
  3. 3Center for Human Nutrition, University of California, Los Angeles, Los Angeles, California;
  4. 4Department of Medicine, University of California, Los Angeles, Los Angeles, California;
  5. 5Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
  1. Corresponding author: Simin Liu, siminliu{at}ucla.edu.

Decades of research have identified numerous biomarkers for cardiovascular diseases (CVDs) and type 2 diabetes, providing molecular insights for improved treatment and prevention of the diseases (13). Of the biomarkers that could be objectively and systematically measured, genetic variants such as single nucleotide polymorphisms (SNPs) have some unique features in that they do not change over time, and the temporal sequence of genotype-phenotype can be clearly established for outcome prediction.

Using high-density fixed SNP arrays, recent genome-wide association studies (GWAS) have successfully identified multiple risk alleles related to CVD and type 2 diabetes. These advances in genomics present many exciting opportunities in three scientific domains: 1) integrating novel genetic variants into risk prediction models of complex diseases in humans, 2) characterizing new biological pathways involved in pathogenesis and thus improved strategies for treatment and management, and 3) enhancing inference of traditional epidemiological work relevant to public health importance. To capitalize on these opportunities, several groups have attempted to develop genetic risk scores by summing up the number of risk alleles for disease prediction. However, almost all these studies have concluded that current genetic information contributes little information in distinguishing who will or will not develop a CVD or type 2 diabetes among apparently healthy adults (46).

Given that most common risk variants identified so far confer relatively modest risk to these complex diseases (e.g., all risk alleles for type 2 diabetes identified by GWAS have very small relative risks [<1.50]) (7,8), the “common diseases-common variants” model has been formally challenged (9,10). In the field of complex disease genetics, it is now widely anticipated that some ongoing next-generation sequencing work covering the whole genome in diverse populations would identify rare variants of large effect sizes in the coming years (8). Yet, there …

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